Summary of Approximate Nearest Neighbour Search on Dynamic Datasets: An Investigation, by Ben Harwood et al.
Approximate Nearest Neighbour Search on Dynamic Datasets: An Investigation
by Ben Harwood, Amir Dezfouli, Iadine Chades, Conrad Sanderson
First submitted to arxiv on: 30 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper investigates the performance of approximate k-Nearest Neighbor (ANN) methods on dynamic datasets, which are subject to frequent online changes. Traditional evaluation approaches do not consider the computational costs of updating the index structure and the rate and size of index updates. The authors empirically evaluate five popular ANN methods on two main applications: online data collection and online feature learning. They use two dynamic datasets derived from SIFT1M and DEEP1B, with 1 million and 1 billion samples respectively. The results show that k-d trees are not suitable for dynamic datasets due to their slower performance compared to a baseline exhaustive search method. For online data collection, Hierarchical Navigable Small World Graphs achieve a consistent speedup over the baseline across a wide range of recall rates. For online feature learning, Scalable Nearest Neighbours is faster than the baseline for recall rates below 75%. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how to make search engines work better when data changes quickly. Search engines use something called k-Nearest Neighbor methods to find answers. These methods are usually good for searching large amounts of data, but they can get slow if the data is changing a lot. The authors tested five different methods on two big datasets with 1 million and 1 billion pieces of information. They found that one method, k-d trees, doesn’t work well when the data changes quickly because it takes too long to update its search index. Another method, Hierarchical Navigable Small World Graphs, works better for searching through data as it’s added online. This can help make search engines more efficient and effective. |
Keywords
» Artificial intelligence » Nearest neighbor » Recall
